A Survey on Privacy Preservation Techniques in Social Clustering via Federated Learning and Deep Learning

The rapid progress in information and communications technologies has made personal data a valuable resource, which can help data owners seamlessly meet the needs of their affiliates. Data owners are acquiring enormous and various amounts of personal data due to the widespread proliferation of digital tools and computing. Current efforts mostly concentrate on establishing Federated Learning (FL) methods that are optimized, ignoring the enduring and internal connections between users, like social relationships. This survey helps the researchers analyze several approaches to privacy preservation in social networks. 60 research articles that focus on different techniques for social clustering privacy preservation are analyzed. Moreover, this survey provides an analysis of the available privacy preservation techniques in social clustering on the basis of publication year, research techniques, tools used, dataset used, and performance assessment, along with the accomplishments of research techniques, paving the way for the establishment of innovative models in the future. Various researchers have developed models to address this issue, and the techniques can be categorized based on Deep Learning (DL), FL, and other techniques. The real-world datasets are most widely employed. Moreover, the research gaps and drawbacks of these techniques are discussed, highlighting the need for an effective approach to privacy preservation in social clustering using FL.